A novel application of pattern recognition for accurate SNP and indel discovery from high-throughput data: Targeted resequencing of the glucocorticoid receptor co-chaperone FKBP5 in a Caucasian population

Abstract

The detection of single nucleotide polymorphisms (SNPs) and insertion/deletions (indels) with precision from high-throughput data remains a significant bioinformatics challenge. Accurate detection is necessary before next-generation sequencing can routinely be used in the clinic. In research, scientific advances are inhibited by gaps in data, exemplified by the underrepresented discovery of rare variants, variants in non-coding regions and indels. The continued presence of false positives and false negatives prevents full automation and requires additional manual verification steps. Our methodology presents applications of both pattern recognition and sensitivity analysis to eliminate false positives and aid in the detection of SNP/indel loci and genotypes from high-throughput data. We chose FK506-binding protein 51(FKBP5) (6p21.31) for our clinical target because of its role in modulating pharmacological responses to physiological and synthetic glucocorticoids and because of the complexity of the genomic region. We detected genetic variation across a160 kb region encompassing FKBP5. 613 SNPs and 57 indels, including a 3.3 kb deletion were discovered. We validated our method using three independent data sets and, with Sanger sequencing and Affymetrix and Illumina microarrays, achieved 99% concordance. Furthermore we were able to detect 267 novel rare variants and assess linkage disequilibrium. Our results showed both a sensitivity and specificity of 98%, indicating near perfect classification between true and false variants. The process is scalable and amenable to automation, with the downstream filters taking only 1.5 hours to analyze 96 individuals simultaneously. We provide examples of how our level of precision uncovered the interactions of multiple loci, their predicted influences on mRNA stability, perturbations of the hsp90 binding site, and individual variation in FKBP5 expression. Finally we show how our discovery of rare variants may change current conceptions of evolution at this locus.

FKBP5 genetic variation

“…Our FKBP5 resequencing covered an area of 160 kb on chromosome 6 and identified 657 SNPs (Supplementary Table 4, http://links.lww.com/FPC/A572), as described in detail by Pelleymounter et al [26]. The majority of the polymorphisms, including 44 indels (insertions/deletions), were located in introns, flanking regions, and untranslated regions.…”

“…Both Sanger and Next Generation sequencing were used to resequence FKBP5 (primers listed in Supplementary Table 1, http://links.lww.com/FPC/A572), as described previously [26]. Sanger sequencing was used to resequence all exons, exon–intron splice junctions, and ~1000 bp of the 5′ and 3′ flanking regions using 96 DNA samples from lymphoblastoid cells generated from white American patients included in the ‘Human Variation Panel’ (HD100CAU; Coriell Institute, Camden, New Jersey, USA) [27].…”

Identification of Gene Mutations in Autosomal Dominant Polycystic Kidney Disease through Targeted Resequencing

“…49 Sensitivity, specificity, and accuracy will improve by individually bar coding each sample and by further development of the data mining strategies. [50][51][52] The application of NGS in these experiments has allowed the discovery and characterization of missed and atypical variants (allele dropout, gene conversion, and deep intronic variants). Allele dropout is a well known cause of missed mutations 53 due to unequal amplification of heterozygote alleles.…”

“…NextGENe software assigns a confidence score to each variant call on a scale from 0 to 30, which was used for further variant filtering. 50 Each bar code was mined at 4% mutant allele percentage-1003 read depth (high stringency), 4% mutant allele percentage-1003 read depth on trimmed, 76-bp long reads (medium stringency), and 3% mutant allele percentage-1003 read depth (low stringency). These three mining protocols were typically linked within NextGENe for each bar code, and several bar codes linked in series, for automatic execution.…”

Contribution of FKBP5 Genetic Variation to Gemcitabine Treatment and Survival in Pancreatic Adenocarcinoma

“…Resequencing covered a 160 kb genomic region on chromosome 6p21 that contained FKBP5 utilizing methods described previously [14]. Sanger sequencing of FKBP5 was used for validation purposes and was performed with the same DNA samples.…”